Whales, dolphins and porpoises are difficult to detect visually from the water surface, but they produce a rich variety of underwater acoustic signals, which offer a means for detecting their presence and identifying them to species. In recent years, Passive Acoustic Monitoring (“PAM”) has proven to be one of the most effective methods for determining species occurrence and distributions over various spatial and temporal scales. PAM is motivated by the need to estimate and evaluate the potential impacts of human noise generating activities on marine mammals, where impacts range from acute—such as auditory effects—to chronic—such as communication masking.
PAM methods have been applied to successfully detect various species, for example, baleen whale species in North American waters. However, there are several challenges to using PAM. First, understanding migratory patterns and seasonal behaviors requires the collection of long-term acoustic recordings. Second, background noise levels can vary significantly throughout the data collection period, increasing the difficulty of sound detection and classification analysis. Together, variable noise and enormous quantities of acoustic data become a formidable challenge for detecting species such as the highly endangered North Atlantic Right Whale. To optimize this process, automated signal processing—otherwise known as data mining methods—has been developed to accurately and precisely detect whales while accounting for variability in signal structure and background noise. In most cases, sound recognition algorithms analyze sound data after the recording devices are recovered from the water. New technologies have begun to incorporate signal recognition algorithms in a real-time system while devices are recording. In these different archival and real-time applications, signal processing methods are used to detect sounds of interest. Tools for successful PAM maximize the number of true whale detections while minimizing missed whale sounds and false positives. However, temporal and geographical fluctuations in ambient and anthropogenic noise levels can bias the detection process when using conventional tools.
Marine environments are being subjected to increasing levels of noise. To measure and understand the water's acoustic environment, both impulsive and chronic noise sources must be considered. Of particular interest are noise sources generated by seismic vessels and ship traffic. In order to understand acute and chronic impacts, several parameters are required, including noise measures, animal locations, sound characterization and environmental factors, such as ocean conditions and vessel location. To make informed decisions, information needs to be systematically processed in order to provide metrics for estimating chronic and acute sounds. To date, few software packages integrate all these parameters.
One method to measure sound exposure from acute sources is known as the Acoustic Integration Model (“AIM”). AIM works to model both stationary and mobile objects using actual track information or simulated information. However, recent trends in understanding noise go beyond acute sources and consider chronic impacts. Chronic impacts require understanding long-term trends in the environment, and engage a paradigm that considers signal excess and communication space as critical quantities for evaluating the influence of cumulative anthropogenic sound sources on the ability for marine mammals to communicate.
Therefore, there is a need to measure and quantify the communication space of animals. Specifically, there is a need to integrate a variety of data formats in a relatively short amount of time, provide advanced computing environments, and leverage existing tools, provide mechanisms to integrate various data layers, and produce situational models that describe the acoustic environment. The present invention satisfies this need.